Bellevue, Washington, United States
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About

I am the National Services Market Leader for Public Sector at Insight Solutions, a…

Experience & Education

  • Insight

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Volunteer Experience

  • Board of Directors

    Group Health Foundation

    - 9 years

Publications

  • Well-Test Model Identification With Self-Organizing Feature Map

    Society of Petroleum Engineers - Peer Reviewed Journal

    Well-test data has been traditionally used for determining a variety of reservoir parameters, such as average permeability, storage capacity, reservoir damage, presence of faults and fractures, reservoir mechanism, etc. A number of techniques, both conventional methods such as the curve matching and numerical simulation, and artificial intelligence methods have been used for identifying well test models. These methods are laborious and time consuming and at times give in-correct…

    Well-test data has been traditionally used for determining a variety of reservoir parameters, such as average permeability, storage capacity, reservoir damage, presence of faults and fractures, reservoir mechanism, etc. A number of techniques, both conventional methods such as the curve matching and numerical simulation, and artificial intelligence methods have been used for identifying well test models. These methods are laborious and time consuming and at times give in-correct results.

    Artificial neural networks (ANN) are recent development in computer vision and image analysis. These are specialized computer software that generate a strategy to produce non-linear mapping functions for complex problems. ANN are commonly used as a tool for recognizing an object or predicting an event given an associated pattern. Only a limited number of applications of ANN for analyzing well test data have been reported. These methods are mostly model-specific (developed for specific reservoir models) and hence are not general enough.

    This paper presents a new method based on ANN that uses Kohonen's self organizing feature (SOF) mapping technique to identify well test interpretation models. By grouping well test data into distinct categories the SOF algorithm produces a general mapping function. This method can help analyze well test data from a large variety of reservoirs (including reservoirs with faults, fractures, boundaries, etc.) more efficiently and inexpensively than was previously feasible.

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